FIG. 1 is a conventional base band-equivalent model 100 of a communication link for a TDD-based communication system. Signal d is formatted (burst composition 10) according to the relevant standard, e.g., Third Generation Partnership Project (3GPP) Low Chip Rate Time Division Duplex (LCR-TDD) system, 3GPP High Chip Rate Time Division Duplex (HCR-TDD) system and Global System for Mobile Communication (GSM). The formatted signal is then passed to a filter 11, which is normally a root-raised cosine (RRC) filter for shaping the transmitted pulse. This is a typical filter used in conventional communication systems.
The pulse-shaped signal is transmitted to a channel 12, which could be wired or wireless. The channel 12 has a fading component as well as a noise component. The fading component hw normally introduces a time delay of W chips, whilst the noise component n could be modeled as additive white Gaussian noise (AWGN). A typical fading scenario that causes delay spread is multi-path fading. Due to the effect of delay, inter-symbol interference (ISI) exists in the received signal.
In the receiver, a similar RRC filter 13 is used to match the transmitter RRC filter 11. The filtered signal e is passed to an equalizer 14 to remove the ISI effects. Channel estimates ĥw are provided by a channel estimation unit 15.
As an example of a TDD system, an LCR-TDD system uses a frame length of 10 ms, and each 10 ms frame is divided into two sub-frames of 5 ms. Each sub-frame 200 includes seven normal time slots (see FIG. 2), and each slot is assigned either to uplink (or reverse link) or to downlink (or forward link). In FIG. 2, Tsn (n from 0 to 6) is the nth traffic time slot, 864 chips duration. DwPTS is: downlink pilot time slot, 96 chips duration. UpptS is: uplink pilot time slot, 160 chips duration and GP is: main guard period for TDD operation, 96 chips duration.
As shown in FIG. 3, the normal time slot structure 300 of Tsn includes two data parts 302a and 302b in each slot, separated by a midamble 304, which is used for channel estimation.
FIGS. 4(a)-(b) illustrate plots 400a, 400b and 400c the effect of fading on the received signal. FIG. 4(a) shows a band-limited pulse having zeros periodically spaced in time at points labeled ±T, ±2T, . . . . For an LCR TD-SCDMA system, T is equal to 0.78125 μs. Transmission of the pulse through a fading channel, however, results in the received pulse shown in FIG. 4(b) having zeros crossing that are no longer periodically spaced. FIG. 4(c) illustrates the output of a linear equalizer that compensates for the linear distortion in the channel.
As an example of training sequence interference in a TDD system, midamble interference is discussed as follows. An LCR TD-SCDMA timeslot 500 is shown as FIG. 5. The length of the channel window (the delay spread of the channel) is W. E1 and E2 respectively include first and second 352-chip wide data blocks. The length of both E1 and E2 is 352+W−1 chips, to account for the fact that, due to the delay spread of W chips, the 352 chips of E1 data and the 352 chips of E2 data may need to be recovered from respective portions of the time slot that are each 352+W−1 chips wide. Because the midamble lies in the middle of the time slot, and W is larger than 1 (e.g., 16), the midamble will interfere with E1 and E2. The effect of the midamble interference 502a and 502b is shown as the shaded part of FIG. 5.
The midamble interference 502a on the first data part 504a can be written as:I1=H1M1  (Eqn. 1)
In Equation 1, H1 is a matrix consisting of the elements of the channel impulse response estimation, with dimension (W−1)×(W−1). H1=H1(i, j) can be written as:
                                          H            1                    ⁡                      (                          i              ,              j                        )                          =                  {                                                                      h                  ⁡                                      (                                          i                      -                      j                                        )                                                                                                                    for                    ⁢                                                                                  ⁢                    0                                    ≤                                      i                    -                    j                                    ≤                                      W                    -                    1                                                                                                      0                                            else                                                                        (                  Eqn          .                                          ⁢          2                )            
In Equation 2, h(i−j), 0=<i−j<=W−1 is the estimation of channel impulse response and M1=[m(0), m(1), . . . m(W−2)]T is a vector with dimension W−1, and m(i) (0=<i<=W−2) is the elements of midambles.
The midamble interference 502b on the second data part 504b could be written as shown in Equation 3 below.I2=H2M2  (Eqn. 3)
In Equation 3, H2 is a matrix consisting of the elements of the channel impulse response estimation, with dimension (W−1)×(W−1). H2=H2(i, j) could be written as shown in Equation 4 below.
                                          H            2                    ⁡                      (                          i              ,              j                        )                          =                  {                                                                      h                  ⁡                                      (                                          W                      -                      1                      +                      i                      -                      j                                        )                                                                                                                    for                    ⁢                                                                                  ⁢                    0                                    ≤                                      j                    -                    i                                    ≤                                      W                    -                    1                                                                                                      0                                            else                                                                        (                  Eqn          .                                          ⁢          4                )            
For the last W−1 chips of block E1 could be written as shown in Equation 5 below.E1=H1*D+I1+n  (Eqn. 5)
The four W−1 chips of block E2 can be written as shown in Equation 6 below.E2=H2*D2+I2+n  (Eqn. 6)
In Equation 6, D is the transmitted data, and n is white noise. If I1 and I2 are ignored, the error probability will increase for the last W−1 chips of estimated data block {circumflex over (d)}1 and the first W−1 chips of the estimated data block {circumflex over (d)}2.
A conventional equalizer could reduce the degrading effect of ISI. The equalizer is supplied with an estimate of the channel impulse response ĥw associated with the propagation channel.
In the downlink, all signals are distorted by the same propagation channel. This multi-path propagation channel destroys the orthogonality of the spreading codes and therefore causes multiple-access interference (MAI). To a large extent, the orthogonality of user codes can be restored and MAI can be suppressed by employing channel equalization.
The structure of the conventional equalizer 14 for the downlink is shown in FIG. 6. Conventional equalizer 14 includes an RRC Filter 602, an extract data symbol block 604, a midamble interference cancellation block 608 and an equalizing block 610. Equalizer 14 also includes an extract midamble block 612 and channel estimation block 614. Equalizer 14 also includes a despreader block 616 and Qpsk demodulate block 618.
Assuming the channel equalizer is linear and can be represented by the estimation matrix Â, which is independent of user codes, the estimate of the sum vector for data Ŝd could be written as shown in Equation 7 below.Ŝd=Âe  (Eqn. 7)
The choice of Â determines the channel equalizer types. Three most common types are listed below as Equations 8-10.Matched filtering: ŜMF=HH*e  (Eqn. 8)Zero forcing: ŜZF=(HH*H)−1HH*e  (Eqn. 9)Minimum mean square error: ŜMMSE=(HH*H+σ2)−1HH*e  (Eqn. 10)
In order to avoid complex receiver processing tasks such as Cholesky decomposition for solving matrix inversions, channel equalization can be performed efficiently in the frequency domain using fast Fourier transform (FFT) as follows
                                          s            ^                    MF                =                              F                          -              1                                ⁡                      (                                                            (                                      F                    ⁡                                          (                                              h                        ^                                            )                                                        )                                *                            *                              F                ⁡                                  (                  e                  )                                                      )                                              (                  Eqn          .                                          ⁢          11                )                                                      s            ^                    ZF                =                              F                          -              1                                ⁢                                                    (                                  F                  ⁡                                      (                                          h                      ^                                        )                                                  )                            *                                      (                                                                    F                    ⁡                                          (                                              h                        ^                                            )                                                        *                                *                                  F                  ⁡                                      (                                          h                      ^                                        )                                                              )                                ⁢                      F            ⁡                          (              e              )                                                          (                  Eqn          .                                          ⁢          12                )                                                      s            ^                    MMSE                =                              F                          -              1                                ⁢                                                    (                                  F                  ⁡                                      (                                          h                      ^                                        )                                                  )                            *                                                      (                                                                            F                      ⁡                                              (                                                  h                          ^                                                )                                                              *                                    *                                      F                    ⁡                                          (                                              h                        ^                                            )                                                                      )                            +                              σ                2                                              ⁢                      F            ⁡                          (              e              )                                                          (                  Eqn          .                                          ⁢          13                )            
In Equations 11-13, F denotes the FFT, F−1 is the inverse FFT and * denotes the complex conjugate.
A conventional equalizer with MIC (midamble interference cancellation) 700 is depicted in FIG. 7. Instead of passing 352 data chips to the equalizer, an additional W−1 chips are required due to the effect of delay spread. If these 352+W−1 data chips are passed to the equalizer directly, the data chips in the overlap regions between the data 702a and 702b and midamble 704 are likely to contain more interference. Therefore, normally a MIC scheme is necessary to remove the interference before passing the ‘cleaner’ data for subsequent processing.
Before equalizing, midamble interference cancellation is performed, that isE1′=E1−I1  (Eqn. 14)E2′=E2−I2  (Eqn. 15)
Under the condition of beam forming, there are different channels corresponding to different users. At most, there are 16 different radio channels. In this case, the above MIC operation has to be executed 16 times, which will cost around 2-3 million instructions per second (MIPS) in a typical dual-MAC DSP. In addition, since channel estimation is used for assisting MIC, errors in channel estimation will propagate into errors in MIC, thus, introducing additional error into the whole system.
The following documents are incorporated herein by reference:    [1] Ingolf Held and Almansor Kerroum, “TD-SCDMA Mobile Station Receivers: Architecture, Performance, Impact of Channel Estimation” in Proc China Wireless Congress, 2002; and    [2] B. Steiner and P. W. Baier, “Low Cost Channel Estimation in the Uplink Receiver of CDMA Mobile Radio Systems”, Frequenz, Vol. 47, pp. 292-298, November/December 1993.
There is therefore a need for improved systems and methods to provide a low-complexity, high performance solution that can remove training sequence interference in TDD communication system receivers.